/library/oar/handle/123456789/5627 2025-12-23T10:05:49Z 2025-12-23T10:05:49Z Garg, Deepak Sharma, Devendra Kumar Garg, Lalit /library/oar/handle/123456789/141340 2025-11-17T08:53:22Z 2025-01-01T00:00:00Z

Title: Energy efficient memory architectures for next-generation wearable healthcare devices Authors: Garg, Deepak; Sharma, Devendra Kumar; Garg, Lalit Abstract: The paper explores low-power design strategies for SRAM cells in wearable and implantable devices (WIDs) to address critical power limitations that hinder further miniaturization. FinFET solves the problem of leakage current (I_Leakage) by improving the challenging power versus performance trade-off. This research develops 7-Transistor SRAM cells based on FinFETs using the Multi Threshold CMOS (MTCMOS) and Upper Self Controllable Voltage Level (USVL) methods. Using 45 nm FinFET technologies, the design and simulation of all design circuits are carried out with Cadence Virtuoso. The work adopts a multi-disciplinary approach, combining device-circuit co-design to achieve ultra-low-power operations suitable for complex tasks in wearable and implantable micro systems. The proposed design shows that the USVL method of a 7T SRAM using FinFET is more effective than the MTCMOS methodology in terms of leakage power and leakage currents. Additionally, among other proposed approaches, a comparative analysis of leakage currents and leakage power is conducted. Key outcomes include significant improvements in leakage power through FinFET-based SRAM cell using USVL technique. This paper contributes to advancing low-leakage wearable/ implantable devices (WIDs) by integrating innovative leakage reduction techniques with cutting-edge low-power circuit designs. The proposed design achieves a minimum leakage current of 10.6 nA and leakage power of 26.98nW by utilizing USVL approach. Compared to SRAM cells designed with the MTCMOS technique, the proposed method results in approximately 17.8% and 28% reduction in leakage power and leakage current, respectively. The findings pave the way for developing smaller, smarter, and sustainable wearable and implantable devices capable of complex tasks without reliance on batteries.

2025-01-01T00:00:00Z Yadav, Kusum Alkwai, Lulwah M. Almansour, Shahad Siddiqui, Malika Anwar Sharma, Devendra Kumar Garg, Lalit Goswami, Pratik Alkhayyat, Ahmed Hussein /library/oar/handle/123456789/141316 2025-11-14T13:46:00Z 2025-01-01T00:00:00Z

Title: An enhanced rule-based fuzzy segmentation approach for automated urban feature extraction using high-resolution satellite imagery Authors: Yadav, Kusum; Alkwai, Lulwah M.; Almansour, Shahad; Siddiqui, Malika Anwar; Sharma, Devendra Kumar; Garg, Lalit; Goswami, Pratik; Alkhayyat, Ahmed Hussein Abstract: A fuzzy segmentation approach based on rule-based fuzzy rules is proposed in this study to obtain urban features using high-resolution satellite images. Multiresolution segmentation and spectral difference segmentation are combined to effectively identify and classify houses, roads, trees, and agricultural fields in urban areas and rural farms.Afuzzy rule setwas developed using satellite datasets from IKONOS, LISS IV, and WorldView-2, improving classification accuracy. In this work, buildings were extracted from IKONOS images, agricultural fields were extracted from LISS IV images, and roads and vegetation were extracted from WorldView-2 images. The map updating capability was demonstrated for 1:2500 and 1:1000 scales, respectively, for buildings and agricultural fields. Furthermore, the gray-level cooccurrence matrix was employed to enhance classification reliability and mitigate spectral confusion. By automating the process, the need for additional GIS data is reduced, making it a cost-effective, scalable, and efficient approach. Compared to traditional manual feature extraction methods, this method is an effective alternative in urban planning, land use mapping, and environmental monitoring.

2025-01-01T00:00:00Z Ye, Bo Shu, Zhi Wang, Shufang Wang, Bo Gao, Guangzheng Dai, Shoujun Liu, Sixian Xu, Yihan Randhawa, Princy Garg, Lalit Krishna Dwivedi, Amit Liu, Sheng /library/oar/handle/123456789/141286 2025-11-14T07:26:26Z 2025-01-01T00:00:00Z

Title: Design and control of a dual-drug-chamber drug delivery capsule robot for multi-target drug delivery Authors: Ye, Bo; Shu, Zhi; Wang, Shufang; Wang, Bo; Gao, Guangzheng; Dai, Shoujun; Liu, Sixian; Xu, Yihan; Randhawa, Princy; Garg, Lalit; Krishna Dwivedi, Amit; Liu, Sheng Abstract: With the advancement of capsule endoscopy technology, the treatment of gastrointestinal (GI) tract diseases has entered a new era. Capsule robots enable the diagnosis and treatment of lesions in a painless and minimally invasive manner. Research on highly controllable Drug Delivery Systems (DDSs) using capsule endoscopy is significant for treating GI tract diseases. Despite the variety and complexity of DDS designs, most systems lack the capability for multi-target drug delivery and simultaneous carriage of multiple drugs. This paper proposes a dual-drug-chamber drug delivery capsule robot (DDCR) that utilizes a single Internal Permanent Magnet (IPM) for both drug delivery and propulsion. The design of the dual-drug-chamber primarily aims to carry one or two drugs simultaneously for treating one or multiple target sites. The proposed DDCR uses a balloon mechanism for drug containment. An external magnetic drive system activates a needle attached to a piston, which punctures the balloon and, thereby, releases the medication. This mechanism ensures rapid and effective coverage of lesions by the drugs. Based on both theoretical and experimental results regarding balloon drug loading and the control distance of the external permanent magnet (EPM) control distance, it was established that a drug load of 0.3 ml per chamber was most suitable for the design, and the feasibility of the dosing method was demonstrated. The size (29mm in length and ~13mm in diameter) of the DDCR was also found to be suitable for biological experiments, including multi-target drug delivery. By continuously adjusting the distance between the DDCR and the EPM in porcine small intestine samples, the optimal driving distance and drug delivery distance were found to be 80mm and 140mm. Note to Practitioners—The proposed dual-drug-chamber DDCR can potentially address the limitations of current systems in multi-drug and multi-target GI treatments. The use of a single internal permanent magnet (IPM) for both propulsion and drug release enables precise, minimally invasive delivery of one or two drugs to different target sites within the GI tract. Its promising design with a compact structure and balloon-based drug release mechanism provides practical value for treating conditions like Crohn’s disease and gastric ulcers and offers greater flexibility and control compared to traditional methods. The external magnetic control system ensures accurate positioning and drug release, making the DDCR a promising tool for targeted therapy. Despite its potential, there are practical limitations, including the need for enhanced real-time control and adaptation to various patient anatomies. The current study demonstrates feasibility, but further research is necessary to refine the system’s accuracy in dynamic GI environments and ensure its effectiveness in clinical applications. These developments could help advance capsule endoscopy and drug delivery technology in the near to mid-term, providing more efficient and patient-friendly treatment options.

2025-01-01T00:00:00Z Lingaraj, K. Malghan, Rashmi Laxmikant Rao Mc, Karthi K. Garg, Lalit Somanath Swamy, R. H. M. Vishwanatha, H. M. /library/oar/handle/123456789/141278 2025-11-13T13:59:21Z 2025-01-01T00:00:00Z

Title: FLPSO-AMPS : an optimized WSN model for air quality monitoring in tier-2 smart cities Authors: Lingaraj, K.; Malghan, Rashmi Laxmikant; Rao Mc, Karthi K.; Garg, Lalit; Somanath Swamy, R. H. M.; Vishwanatha, H. M. Abstract: Wireless Sensor Networks (WSNs) are composed of small, cost-effective sensing nodes that are primarily employed for the collection of environmental data. These networks are integral to various applications including industrial pollution monitoring, disaster management, and air quality regulation. However, WSNs encounter significant challenges, such as energy efficiency, end-to-end delay, and packet loss during data transmission. Existing methodologies often fall short in optimizing the network lifespan while ensuring reliable data delivery. To address these limitations, this study introduces FLPSO-AMPS, a novel Fuzzy Logic-based Particle Swarm Optimization (FLPSO) approach aimed at enhancing energy-efficient routing in WSN-based Air Pollution Monitoring Systems (APMS) for Tier-2 smart cities. The proposed approach leverages fuzzy logic principles combined with PSO to intelligently select optimal routing paths, thereby ensuring minimal energy consumption and enhanced network longevity. Unlike conventional methodologies, FLPSO-AMPS incorporates realtime pollutant data collection and mobility-aware optimization to improve network performance. The effectiveness of FLPSO-AMPS was validated through extensive simulations, demonstrating superior performance over existing approaches, particularly with improvements of 10% in energy efficiency, 15% in task delay, 24.5% in packet delivery ratio (PDR), 11.5% in packet loss ratio (PLR), and 20.1% in throughput. These findings underscore the potential of FLPSO-AMPS in establishing an intelligent, resource-efficient air quality monitoring framework for smart cities. Future research will explore security enhancements to safeguard data transmissions in APMS networks.

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